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Pancreatic cancer has become the third leading cause of cancer-related death in the Western world despite advances in therapy of other cancerous lesions. Late diagnosis due to a lack of symptoms during early disease allows metastatic spread of the tumor. Most patients are considered incurable because of metastasized disease. On a cellular level, pancreatic cancer proves to be rather resistant to chemotherapy. Hence, early detection and new therapeutic targets might improve outcomes. The detection of DNA promoter hypermethylation has been described as a method to identify putative genes of interest in cancer entities. These genes might serve as either biomarkers or might lead to a better understanding of the molecular mechanisms involved. We checked tumor specimens from 80 patients who had undergone pancreatic resection for promoter hypermethylation of the zinc finger protein ZNF154. Then, we further characterized the effects of ZNF154 on cell viability and gene expression by in vitro experiments. We found a significant association between ZNF154 hypermethylation and better survival in patients with resectable pancreatic cancer. Moreover, we suspect that the cell growth suppressor SLFN5 might be linked to a silenced ZNF154 in pancreatic cancer.

Motivation: Disease progression in cancer can vary substantially between patients. Yet, patients often receive the same treatment. Recently, there has been much work on predicting disease progression and patient outcome variables from gene expression in order to personalize treatment options. A widely used approach is high-throughput experiments that aim to explore predictive signature genes which would provide identification of clinical outcome of diseases. Microarray data analysis helps to reveal underlying biological mechanisms of tumor progression, metastasis, and drug-resistance in cancer studies. Despite first diagnostic kits in the market, there are open problems such as the choice of random gene signatures or noisy expression data. The experimental or computational noise in data and limited tissue samples collected from patients might furthermore reduce the predictive power and biological interpretability of such signature genes. Nevertheless, signature genes predicted by different studies generally represent poor similarity; even for the same type of cancer. Integration of network information with gene expression data could provide more efficient signatures for outcome prediction in cancer studies. One approach to deal with these problems employs gene-gene relationships and ranks genes using the random surfer model of Google's PageRank algorithm. Unfortunately, the majority of published network-based approaches solely tested their methods on a small amount of datasets, questioning the general applicability of network-based methods for outcome prediction. Methods: In this thesis, I provide a comprehensive and systematically evaluation of a network-based outcome prediction approach -- NetRank - a PageRank derivative -- applied on several types of gene expression cancer data and four different types of networks. The algorithm identifies a signature gene set for a specific cancer type by incorporating gene network information with given expression data. To assess the performance of NetRank, I created a benchmark dataset collection comprising 25 cancer outcome prediction datasets from literature and one in-house dataset. Results: NetRank performs significantly better than classical methods such as foldchange or t-test as it improves the prediction performance in average for 7%. Besides, we are approaching the accuracy level of the authors' signatures by applying a relatively unbiased but fully automated process for biomarker discovery. Despite an order of magnitude difference in network size, a regulatory, a protein-protein interaction and two predicted networks perform equally well. Signatures as published by the authors and the signatures generated with classical methods do not overlap -- not even for the same cancer type -- whereas the network-based signatures strongly overlap. I analyze and discuss these overlapping genes in terms of the Hallmarks of cancer and in particular single out six transcription factors and seven proteins and discuss their specific role in cancer progression. Furthermore several tests are conducted for the identification of a Universal Cancer Signature. No Universal Cancer Signature could be identified so far, but a cancer-specific combination of general master regulators with specific cancer genes could be discovered that achieves the best results for all cancer types. As NetRank offers a great value for cancer outcome prediction, first steps for a secure usage of NetRank in a public cloud are described. Conclusion: Experimental evaluation of network-based methods on a gene expression benchmark dataset suggests that these methods are especially suited for outcome prediction as they overcome the problems of random gene signatures and noisy expression data. Through the combination of network information with gene expression data, network-based methods identify highly similar signatures over all cancer types, in contrast to classical methods that fail to identify highly common gene sets across the same cancer types. In general allows the integration of additional information in gene expression analysis the identification of more reliable, accurate and reproducible biomarkers and provides a deeper understanding of processes occurring in cancer development and progression.:1 Definition of Open Problems 2 Introduction 2.1 Problems in cancer outcome prediction 2.2 Network-based cancer outcome prediction 2.3 Universal Cancer Signature 3 Methods 3.1 NetRank algorithm 3.2 Preprocessing and filtering of the microarray data 3.3 Accuracy 3.4 Signature similarity 3.5 Classical approaches 3.6 Random signatures 3.7 Networks 3.8 Direct neighbor method 3.9 Dataset extraction 4 Performance of NetRank 4.1 Benchmark dataset for evaluation 4.2 The influence of NetRank parameters 4.3 Evaluation of NetRank 4.4 General findings 4.5 Computational complexity of NetRank 4.6 Discussion 5 Universal Cancer Signature 5.1 Signature overlap – a sign for Universal Cancer Signature 5.2 NetRank genes are highly connected and confirmed in literature 5.3 Hallmarks of Cancer 5.4 Testing possible Universal Cancer Signatures 5.5 Conclusion 6 Cloud-based Biomarker Discovery 6.1 Introduction to secure Cloud computing 6.2 Cancer outcome prediction 6.3 Security analysis 6.4 Conclusion 7 Contributions and Conclusions

Additional file 1: Figure S1. Differential levels of circulating proteins in participants with IPF versus controls. Volcano plot of the Log2fold change in means by log10 of the corrected p Value for each protein. The horizontal line indicates the threshold for statistical significance. Figure S2. Histogram of the linear discriminant scores for each participant in the IPF and control cohort. Table S1. Summary statistics for all 1305 proteins assayed across the IPF and control cohorts. Protein data are reported in relative fluorescent units. Table S2. Operating characteristics of all models in the test set for the IPF versus control multivariable modelling. Table S3. Proteins designated as among the most influential in at least two of the eight multivariable models. Table S4. Proteins significantly associated with FVC % predicted (unadjusted and adjusted for anti-fibrotic treatment). Table S5. Proteins significantly associated with DLco % predicted (unadjusted and adjusted for anti-fibrotic treatment). Table S6. Proteins significantly associated with composite physiologic index (unadjusted and adjusted for anti-fibrotic treatment).

Additional file 1: Figure S1. Differential levels of circulating proteins in participants with IPF versus controls. Volcano plot of the Log2fold change in means by log10 of the corrected p Value for each protein. The horizontal line indicates the threshold for statistical significance. Figure S2. Histogram of the linear discriminant scores for each participant in the IPF and control cohort. Table S1. Summary statistics for all 1305 proteins assayed across the IPF and control cohorts. Protein data are reported in relative fluorescent units. Table S2. Operating characteristics of all models in the test set for the IPF versus control multivariable modelling. Table S3. Proteins designated as among the most influential in at least two of the eight multivariable models. Table S4. Proteins significantly associated with FVC % predicted (unadjusted and adjusted for anti-fibrotic treatment). Table S5. Proteins significantly associated with DLco % predicted (unadjusted and adjusted for anti-fibrotic treatment). Table S6. Proteins significantly associated with composite physiologic index (unadjusted and adjusted for anti-fibrotic treatment).